Merchant recommendations associated with a persona

ABSTRACT

The method of processing an analysis cycle to determine interest merchants may include selecting a seed merchant relevant to a topic interest, identifying consumers that have completed a transaction with the seed merchant to generate a list of identified consumers, determining merchants visited by the identified consumers, scoring all the merchants based on network connectivity, activity, and merchant over-index, updating the seed merchant in response to the list of scored merchants relative to a scoring threshold, and scoring the list of identified consumers based on the number of distinct merchants in transaction and over-indexing. Additionally, the method may further comprise producing a list of updated interest merchants and a list of updated identified consumers, where the updated interest merchants and the updated identified consumers are relevant to the topic interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, claims priority to and thebenefit of, U.S. patent application Ser. No. 15/139,176 filed Apr. 26,2016 and entitled “DETERMINING MERCHANT RECOMMENDATIONS.” The '176application is a continuation of, claims priority to and the benefit of,U.S. Pat. No. 9,361,627 issued Sep. 7, 2016 (aka U.S. patent applicationSer. No. 13/734,693 filed on Jan. 4, 2013 and entitled “SYSTEMS ANDMETHODS DETERMINING A MERCHANT PERSONA.)” The '693 application is acontinuation of, claims priority to and the benefit of, U.S. Pat. No.9,195,988 issued Nov. 24, 2015 (aka U.S. patent application Ser. No.13/715,792 filed on Dec. 14, 2012 and entitled “SYSTEMS AND METHODS FORAN ANALYSIS CYCLE TO DETERMINE INTEREST MERCHANTS”), and U.S. patentapplication Ser. No. 13/715,770 filed on Dec. 14, 2012 and entitled“SYSTEMS AND METHODS DETERMINING A MERCHANT PERSONA.” The '792 and '770applications each claim priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 61/610,461 filed on Mar. 13, 2012 andentitled “GENERATING MERCHANT RECOMMENDATIONS FOR CONSUMERS;” and U.S.Provisional Patent Application Ser. No. 61/646,778 filed on May 14, 2012and entitled “SYSTEMS AND METHODS FOR TAILORED MARKETING BASED ONFILTERING;” and U.S. Provisional Patent Application Ser. No. 61/700,850filed on Sep. 13, 2012 and entitled “SYSTEMS AND METHODS FOR AN ANALYSISCYCLE TO DETERMINE INTEREST MERCHANTS.” All of which are herebyincorporated by reference in their entirety for all purposes.

FIELD

The present disclosure generally relates to filtering, and moreparticularly, to processing an analysis cycle to determine interestmerchants.

BACKGROUND

With the proliferation of the Internet, as well as the growingpopularity of mobile communication devices, marketplaces in which deals(e.g., offers) are exchanged (e.g., purchased and/or sold) have grownover time. This “deals marketplace” has grown quickly, but hasencountered a number of challenges. For example, offers are usuallypoorly tailored to consumers (e.g., these offers are irrelevant or lessrelevant to consumers), which may lead many consumers to opt out of anoption to receive such offers and/or a tendency of consumers to “tuneout” or ignore offers that are received. Attempts to tailor offers togeneral interests (e.g., “sports” or “automotive”) may be too generic togenerate a response from a consumer. Furthermore, recommendations ofmerchants to consumers based on generic interests or poorly tailoredinformation results in the recommendations having lower creditabilityand being ignored or not valued. It would therefore be advantageous tohave a system in which one or more merchants are able to accuratelytailor relevant offers to one or more consumers. Likewise, it would beadvantageous to have a system in which merchant recommendations are moreaccurately determined and provided to the consumers.

SUMMARY

The present disclosure includes a system, method, and/or article ofmanufacture (collectively, “systems”) for processing an analysis cycleto determine interest merchants. In various embodiments, the method maycomprise selecting seed merchant relevant to a topic interest,identifying consumers that have completed a transaction with the seedmerchant to generate a list of identified consumers, determiningmerchants (e.g., all merchants) visited by the identified consumers,scoring the merchants based on at least one of network connectivity,activity, and merchant over index, where the scoring generates a list ofscored merchants, updating the seed merchant in response to the list ofscored merchants relative to a scoring threshold, and scoring the listof identified consumers based on at least one of a number of distinctmerchants in transaction and over-indexing. In various embodiments, themethod may further comprise determining whether to perform a secondanalysis cycle in response to a turnover index. Additionally, the methodmay further comprise producing a list of updated interest merchants anda list of updated identified consumers, where the updated interestmerchants and the updated identified consumers are relevant to the topicinterest.

In various embodiments, a group of boost consumers may also bedetermined. The group of boost consumers may be determined by evaluatingconsumer transactions across a consumer database, and retrievingconsumers that have transactions with two or more distinct merchants.The determination of boost consumers may be included in, and broaden,the step of determining all the merchants visited by the identifiedconsumers.

In various embodiments, the systems are capable of providingrecommendations. The systems may be capable of determining a pluralityof personas for a first plurality merchants. The plurality of personasmay be based on predetermined activates, transaction information fromtransaction accounts and/or any other suitable data source (e.g., socialdata). The systems may allow a user to select a persona from theplurality of personas. Based on the selection, the system may determinea subset of total merchants associated with the selected persona andrecommend at least a portion of the subset of merchants based on theselected persona.

The system may also allow a user to provide a preference in addition tothe selected persona. The preference may further define the subset ofmerchants or a portion of the subset of merchants. In variousembodiments, the recommending may be adjusted by any suitable factorssuch as, for example, time of day, location, channel, and/or the like.

The persona may be defined by at least one of a hobby and activity. Thathobby or activity may not be related to the user, but may be of interestto the user as a way to try a new hobby or activity. The system mayfurther refine merchants associated with a persona based on transactioninformation and user information.

In various embodiments, the system may be capable of ranking the subsetof merchants associated with the selected persona. The system may rankthe subset of merchants in any suitable fashion. For example, theranking may be defined by a plurality of offers associated with thesubset of merchants. A first subset of the plurality of offers maycomprise a first redemption channel and a second subset of the pluralityof offers may comprise a second redemption channel. The ranking may bebased on or adjusted by the first redemption channel and/or the secondredemption channel. For example, the first redemption channel mayrequire no additional action from the user to receive a benefit and thesecond redemption channel may require an additional action from the userto receive the benefit. As such, the system may rank the firstredemption channel higher than the second redemption channel to promotebenefits that are easy for the user to receive. Benefits may include anysuitable benefit or reward such as, for example, a monetary credit(e.g., a statement credit, instant discount or rebate, and/or the like)or loyalty points.

In various embodiments, the system may be capable of analyzing thetransaction information from the transaction accounts, where thetransaction accounts are associated with the first plurality ofmerchants. Based on this analysis, the system may determine a secondplurality of merchants associated with the transaction information. Thesecond plurality of merchants may also be selected based on thetransaction information and a SEED merchant. The plurality of totalmerchants may also comprise the first plurality of merchants and thesecond plurality of merchants.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings.

FIG. 1 shows an overview of an analysis cycle in accordance with variousembodiments;

FIG. 2 shows an example of an analysis cycle in accordance with variousembodiments;

FIGS. 3A-3B show the incorporation of consumers related to seedmerchants in an analysis cycle in accordance with various embodiments;

FIG. 4 shows the calculating a preliminary merchant score in an analysiscycle in accordance with various embodiments;

FIGS. 5A-5B show the processing of a preliminary merchant score in ananalysis cycle in accordance with various embodiments;

FIGS. 6A-6D show the including of boost consumers in an analysis cyclein accordance with various embodiments;

FIGS. 7A-7B show the calculating of a consumer score in an analysiscycle in accordance with various embodiments;

FIG. 8 shows a turnover index after multiple iterations of an analysisin accordance with various embodiments;

FIG. 9A shows a user display of merchant personas in accordance withvarious embodiments;

FIG. 9B shows a sample table used to determine merchant personas inaccordance with various embodiments;

FIG. 10A-10B shows a method of determining a merchant persona inaccordance with various embodiments;

FIG. 11 shows an exemplary system diagram, in accordance with variousembodiments; and

FIG. 12 shows a flowchart depicting an exemplary process for tailoring arelevant item to a consumer, in accordance with various embodiments.

DETAILED DESCRIPTION

The present disclosure generally relates to filtering, and moreparticularly, to tailored marketing to consumers based on filtering. Thedetailed description of various embodiments herein makes reference tothe accompanying drawings, which show the exemplary embodiments by wayof illustration. While these exemplary embodiments are described insufficient detail to enable those skilled in the art to practice thedisclosure, it should be understood that other embodiments may berealized and that logical and mechanical changes may be made withoutdeparting from the spirit and scope of the disclosure. Thus, thedetailed description herein is presented for purposes of illustrationonly and not of limitation. For example, the steps recited in any of themethod or process descriptions may be executed in any order and are notlimited to the order presented. Moreover, any of the functions or stepsmay be outsourced to or performed by one or more third parties.Furthermore, any reference to singular includes plural embodiments, andany reference to more than one component may include a singularembodiment.

Transaction data and network connectivity of both merchants andconsumers may be analyzed to generate an interest score for a merchanton a topic at a granular level. The granular level analysis is able tofocus on specific topics, instead of simply general topics. For example,a granular level of analysis can score the relevancy of merchants fortopics such as baseball or tennis, versus simply determining that amerchant is related to sports generally. Further, such a granular levelof analysis is capable of capturing a merchant's network specializing inthe specific topic. The specific topic scoring and network analysis canbe used to create more relevant consumer interactions by having morerelevant results. In accordance with various embodiments, systems andmethods are disclosed for identifying consumers and merchants in asystematic, automated, unsupervised manner, without the need tointerview consumers, and based on initial, meaningful input.

In general, an item (e.g., a merchant and/or an offer) may be tailoredto a consumer based upon a recommender system. In various embodiments,the recommender system (e.g., a collaborative filtering algorithm, aninterest analyzer, and/or the like) may identify items that are relevantto one or more consumers and/or one or more merchants. For example, arecommender system may assign a score to one or more items, where thescore may be based on information such as a consumer profile, atransaction history associated with a consumer, social data (e.g., dataassociated with a social media channel, such as FOURSQUARE, FACEBOOK,TWITTER, and the like), demographic data, clickstream data, consumerfeedback data, merchant profile, merchant transaction history, and thelike. Thus, a score may identify relevant items based upon a variety ofinformation associated with a consumer and/or with a merchant. Moreover,in various embodiments, an item may be tailored to a consumer based on amerchant's needs (e.g., that the merchant is interested in rewardingexisting loyal consumers and/or that the merchant would like to acquirenew consumers). In other embodiments, an item may be tailored to aconsumer based on a consumer's needs. Further still, in variousembodiments, an item may be tailored to a particular consumer based upona business rule, such as, for example, that it is a holiday, that it isa particular time of day, that the consumer is traveling, that the itemis associated with a merchant who is some distance away and/or near tofrom the consumer's location, that the consumer has indicated apreference not to receive the item (e.g., the consumer has given theitem a “thumbs down”), and the like.

As used herein, a “merchant” may be any person or entity capable ofproviding a service or an item. A merchant may distribute the item inany way, for example, by exchanging the item for payment. The merchantmay be capable of accepting the payment through any suitable paymentchannel including traditional payment channels including, for example,POS terminals, online payments terminals, transaction account networksand the like. The merchant may also accept payment throughnon-traditional payment terminals including, for example, social mediachannels, person to person payments. Further information about person toperson payments is described in U.S. patent application Ser. No.13/540,216, entitled Systems and Methods for Transferring Value via aSocial Network, which is herein incorporated by reference in itsentirety.

In various embodiments, and with reference to FIG. 1, an analysis cyclesystem and method 100 may be any system or method for associating orcharacterizing items, consumers, and/or merchants. For example, system100 may be configured to associate items, consumers, or merchants with apersona or a specific interest. System 100 may comprise a process ofreceiving an input seed of a topic interest 1A, processing the inputseed through various steps 1B-1E, and generating interest merchants andenthusiast consumers. An interest merchant may be a merchant determinedto be sufficiently active and/or related to the topic of interest. Aninterest merchant group may be determined by a combination of one ormore groupings, which will be described in greater detail below. Forexample, initially, a list of initial merchants may be retrieved from amerchant database, where a merchant is typically included as a result ofa keyword association. Small merchants that were removed from theinitial merchant list may be reinstated if the small merchants havesufficient consumer activity. Merchants from a consumer booster merchantlist may also be included. The merchants on the consumer boostermerchant list may be added if those merchants have sufficientconnectivity to specific, topic-related consumers to be deemed aninterest merchant. For example, consumer booster merchants may bemerchants that score higher than a threshold value.

With respect to Step 1A of FIG. 1, initial aspects of generating a listof interest merchants and enthusiast consumers include determining aninitial seed. The initial seed, or search input, may include an industrycode, one or more specific merchants, items, offers, behaviors,transaction information and/or keywords (cumulatively referred to asSEED Merchants). The examples discussed below present variousrecommendations based on SEED Merchants, however, those skilled in theart will appreciate that after reading this application, a SEED Customermay also be used. The SEED Merchants are used to identify consumers thathave past transactions with the SEED merchants in order to generate alist of identified consumers. The list of identified consumers is usedto retrieve a list of merchants visited by the identified consumers(Step 1B). The list of merchants may include SEED merchants and non-SEEDmerchants. A time limitation may be included for either the list ofidentified consumers or the list of merchants. For example, the searchesmay be limited to transactions occurring within the past 6 months or thepast 12 months. Limiting the transaction time period maintains therelevancy of the results to current offerings.

Analysis cycle 100 may further comprise merchant scoring (Step 1C) themerchants in the list of merchants. The merchant scoring facilitatesfiltering the list of merchants down to appropriate topic interestmerchants based on a selected topic, thereby generating a scoredmerchant list. The scoring may be based on network connectivity,activity, and merchant over-index. The scoring may take into accountcommon consumers and various connectivity metrics.

In Step 1D, the scored merchant list may then be reduced by removing lowscoring merchants in order to focus the scored merchant list on theselected topic. In various embodiments, merchants may be removed basedon a scoring threshold, within a percentage of the scored merchants, orset number of total merchants. For example, the scoring threshold may beimplemented to remove merchants below the scoring threshold from thescored merchant list. In another example, the process may includekeeping the top N merchants and removing the rest from the scoredmerchant list. In yet another example, the percentage criterion mayinclude keeping a set percentage (e.g., 50%). In a further example, thepercentage criterion may include keeping all merchants within apercentage of the top merchant score, for example all merchants within75% of the top merchants score.

In various embodiments and with reference to Step 1E, analysis cycle 100may include scoring the merchants in the scored merchant list based on anumber of distinct merchants and over-indexing. In operation, analysiscycle 100 quantifies a customer's enthusiasm for a particular interest.More particularly, analysis cycle 100 considers the number of distinctmerchants where a particular customer shops (e.g., transacts) with, andthe merchant strength of each transacting merchant. The merchantstrength or over-indexing, noted above, is the ratio of customers withone or more transactions at a given merchant compared to the baselinepopulation of customers. A larger ratio indicates that the merchantprovides items that are more relevant to the particular item or interest(e.g., the merchant sells more goods and/or services that are related toa SEED merchant). For example, if customer A and customer B both shopfor martial arts supplies, and customer A shops at a merchant with astrength of 100 and customer B shops at a merchant with a strength of25, analysis cycle 100 will assign more points or weight to customer A'sspending behavior (e.g., transactions) with respect to the particularmerchant and the analysis as a whole, when identifying boost merchants.In response to the list of merchants being generated, at Step 1F, theanalysis cycle 100 can either be repeated, or the finalized merchantlist may be used in various other processes.

In accordance with various embodiments, FIG. 2 illustrates an example ofthe described analysis cycle for one cycle 200. The various exemplaryvalues and combinations will be used below to add further disclosure tothe process. In the example, the SEED Merchants are two keywords: coffeeand Company A. The keywords generate a list of merchant that arerelevant to the SEED Merchant. The result in the example, include,Company A (Store 1), Company B, Company C, and Company D. At Step 2A,the Identify Consumers table shows a list of identified consumers whohave past transactions with the SEED Merchants input. In the example,the identified consumers are Ben, Jim, Boris, Monique, Anthony, andKendell. At Step 2B, other merchants visited by the identified consumersare retrieved from a transaction database. Here, the identifiedconsumer, Ben, visited Company E, in addition to the merchantsidentified based on the SEED Merchant. The identified consumer, Jim,also visited Company E and Company F, in addition to the merchantsidentified based on the SEED Merchant. A list of merchants is compiledusing both the SEED Merchants and the additional merchants of Company Eand Company F.

In various embodiments, the list of merchants can be scored in differentways, such as illustrated in Steps 2C1-2C3. Step 2C1 illustrates a tableof merchants scored based on connectivity. The connectivity of amerchant may be determined based on the number of distinct interestmerchants connected to the merchant via common consumers. This score maybe a ‘z-score’ or normalized score (a given value (the number ofdistinct merchants connected by a common customer), subtract the averagescore (the average distinct merchants connected by a common customer),then divide by the standard deviation. For example, in the context ofCompany B, the number of distinct merchants connected by a commoncustomer may be 2, the average distinct merchants connected by a commoncustomer may be 1, and the standard deviation may be 0.816497. As aresult the z-score is (2−1)/0.816497=1.22. The z-score provides multiplemeasures on a common scale (e.g., activity or connectively of large andsmall merchant can be compared on the same scale). The z-score may alsostandardize measurements across multiple interests. For example, thez-score provides threshold values that may be applied to interests withdifferent levels. Golf is an interest where golf enthusiast often shopat multiple golf merchants. A higher number of connectivity is used toseparate golf merchants from non-golf merchants. In contrast, amateurpiloting is an interest where amateur pilots often shop at only a fewaviation merchants. A lower number of connectivity may be used toseparate amateur pilot merchants from non-amateur pilot merchants.Applying a z-score in both situations enables a threshold value to movewith interest since both averages and standard deviations are taken intoaccount when calculating the z-score. The threshold value may be basedon normalized values, and therefore adjusts to the type of interestbeing calculated.

Step 2C2 illustrates a table of merchants scored based on activity. Theactivity of a merchant may be determined by how active the merchant'sconsumers are at other interest merchants. For example, Company C has anactivity score of 2 because its consumer Monique was active with twotransactions at other interest merchants (two transactions at CompanyB). By determining a z-score for Company C (e.g., 0.15), the systemensures that the activity or connectivity comparison is evaluated on thesame scale.

Step 2C3 illustrates a table of merchants scored based on a strengthindex. The strength index of a merchant may be determined by comparingthe percentage of the identified consumers visiting the merchant and thepercentage of a group of control consumers visiting the merchant. In theillustrated table, the column of “% Baseline with ROC” represents apercentage of a group of control consumers visiting the merchant. Forexample, 2.5% of the control consumer group has transactions withCompany A. Therefore, the strength index of Company A is that 50% of theidentified consumers have transactions with Company A, and 2.5% of thegeneral group has Company A transactions, yielding a strength index of20.0 (50%/2.5%).

Step 2D illustrates a step of processing an updated merchant list. Amerchant score can be compiled by summing the merchant scores ofconnectivity, activity, and strength as disclosed in Steps 2C1, 2C2,2C3. Various merchants can be removed or added to the interest merchantlist based on the individual merchant score. For example, merchants withan aggregate score less than −3 may be removed. Merchants with astrength index of greater than 10 may be added to the interest merchantlist.

In addition to scoring merchants, the identified consumers may also bescored. As noted above, the customer's enthusiasm for an interest may becalculated. For example, Ben and Shurti each had at least onetransaction at two seed merchants (e.g., Company A, Company B) and onenew added merchant (Company E). The customer's enthusiasm for theidentified interest or the average merchant strength for each of Ben andShurti may be determined by finding the average strength index for allof the merchants used by Ben or Shurti (e.g., (20 (Company A)+100(Company B)+30 (Company E))/3=50). This value allows the system toquantify how strong a particular consumer's activity is in a particularinterest, as compared to other consumers. In this way, the system mayconsider the strength of the particular consumer's activity when makingrecommendations or providing tailored offers.

After an analysis cycle is completed and merchants are added andremoved, a determination may be made whether to conduct another analysiscycle or output the finalized lists. In the example, the determinationof whether to conduct a second cycle is based on the turnover of thelists. In this example, one merchant (Company E) was added and onemerchant (Company D) was removed. Combining for a turnover of two of thefour initial merchants resulting in a turnover score of 0.50. A turnoverscore threshold may be used in the determination. For example, aturnover score threshold of 0.05 is less than the turnover score of 0.50in the example, and so another analysis cycle will be conducted. Thedifferent in the second cycle is that the identified consumer list andthe interest merchant list are populated from the results of the firstanalysis cycle.

In order to provide a better understanding of the disclosed system andmethod, each of the steps in the analysis cycle will be described ingreater detail. In various embodiments, generating an initial list ofSEED Merchants begins with selecting the initial seed, or search input,which may include an industry code, one or more specific merchants, orkeywords. The selected keywords may be used to generate an initial listof merchants from various data sources. In accordance with variousembodiments, the analysis cycle may access merchant and consumer datarelating to spend data (transaction information for purchases of items),non-transactional data (such as social media), loyalty account activity,location data, internet usage tracking data, user profile data (e.g.,demographic information), and other suitable data (e.g., time of day,season of year, and the like).

In various embodiments, and with respect to FIG. 3A, baseball is thetopic interest and baseball keywords may be used to create an initiallist of merchants (2643 merchants in this example). The initial list ofmerchants may be filtered to generate a list of active merchants bymatching transactions records of a random sample from a consumerdatabase with the initial merchants. In the example, a random sample of5 million consumers from a consumer database having 20 million consumersis used. By cross-referencing the 5 million consumer sample with the2643 merchants, it is determined that 937 merchants have a least onetransaction with a consumer from the random sample. The timeframe ofrelevant transactions may be a design variable as well, for example, twoyears as described in FIG. 3A.

The list of active merchants may be further filtered by deriving whetherthe merchant conducts sufficient consumer activity with distinctconsumers. For example and with reference to FIG. 3B, active merchantswith a limited record of distinct spending consumers, such as less than11, are removed from the merchant list. This threshold may provide otherrelevant information to the system including, for example, whether anidentified merchant is still in business, whether an identified merchantaccepts a particular form of payment (e.g., an American Express Card),and/or the like.

In various embodiments and with reference to FIG. 4, an additionalfiltering step may include calculating a preliminary merchant score inorder to determine the most connected and active merchants remaining inthe merchant list. In various embodiments, the preliminary merchantscore is the sum of a merchant network connectivity (MNC) value and amerchant network activity (MNA) value. The MNC value represents theaverage number of other Interest Merchants shopped at by an interestmerchant's consumers. A specific merchant's MNC value may be calculatedas follows:

${{MNCj} = \frac{{\sum\limits_{i = 1}^{I}\left( {{CDMi} - 1} \right)}:{\left( {{Ci},{Mj}} \right) \in {Tint}}}{{\sum\limits_{i = 1}^{I}1}:{\left( {{Ci},{Mj}} \right) \in {Tint}}}},$where CDMi=Σ_(j=1) ^(J): (Ci, Mj)ϵTint, where Ci is Specific Consumer i;Mj is Specific Merchant j; and Tint is Set of Interest Transactions.

Furthermore, the MNA value represents the notice of transactions made byan interest merchant's consumers at other merchants. A specificmerchant's MNA value may be calculated as follows:MNAj=(Σ_(i=1) ^(I) CROCSi:(Ci,Mj)ϵTint)−MCROCSj,where CROCSi=Σ_(tϵTint)1:(Ct, Mj)ϵTint, and whereMROCSj=Σ_(tϵTint)1:(Ci, Mt)ϵTintwhere Ci is Specific Consumer i; Mj is Specific Merchant j; and Tint isSet of Interest Transactions.

In various embodiments and with reference to FIGS. 5A and 5B, inresponse to the merchants being ranked by the preliminary merchantscore, a preliminary merchant score threshold level is set. Thepreliminary merchant score may be used to determine which merchants mayhave done business with a booster consumer. A booster consumer is aconsumer that is active and connected to the original interest keyword.For example, a booster consumer may be any consumer that conductedtransactions at two or more distinct interest merchants. The boosterconsumer can help facilitate finding other relevant interest merchantsthat may not have been included in the initial merchant list. The otherrelevant interest merchants may have been left off the initial list fora variety of reasons, such as an unrelated business name. In the exampleillustrated in FIG. 5A, the merchants with a higher preliminary scoreare well connected to other interest merchants, and the merchants with alower preliminary score are not well connected to other interestmerchants. The bottom 10% of merchants may be removed as beinginsufficient connected to warrant inclusion in the remaining analysis.

As illustrated in FIG. 5B, a list of booster consumers may be generatedby cross-referencing the remaining interest merchants and a consumerdatabase. The consumer database may include the entire consumer databasefrom which the random consumer sample was selected. Thecross-referencing may return all consumers which meet criteria, such astransactions with at least two distinct interest merchants or any othersuitable rule. The example in FIG. 5B shows 0.10 million boosterconsumers from the cross-referencing of the entire consumer database of20 million consumers.

After a list of booster consumer is generated, non-keyword merchants maybe added to the interest merchant list. In various embodiments and withreference to FIG. 6A-6D, new additions may be made to the current listof interest merchants based on the updated consumer list, which includesthe original consumer list and newly included booster consumers. Alltransactions including for example, the transactions from the consumerson the updated consumer list are initially retrieved, and a list ofmerchants is formed. Typically, and as illustrated by FIG. 6A, the listof merchants is much larger than the pending interest merchant list. Forexample, 763,317 non-keyword merchants are included with the 480 keywordinterest merchants.

In various embodiments and with reference to FIG. 6B, a merchantinterest network score (MINS) facilitates selecting the appropriateinterest merchants from the large list of keyword and non-keywordmerchants. Merchants with a higher MINS are over indexed on consumershave a topic interest, such as baseball. In various embodiments, MINScan be calculated as follows:

${{MINS} = \frac{\begin{matrix}{{\%\mspace{14mu}{interest}\mspace{14mu}{customers}\mspace{14mu}{with}\mspace{14mu} 1\mspace{14mu}{or}}\mspace{14mu}} \\{{more}\mspace{14mu}{transactions}\mspace{14mu}{at}\mspace{14mu} a\mspace{14mu}{merchant}}\end{matrix}}{\begin{matrix}{{\%\mspace{14mu}{of}\mspace{14mu}{baseline}\mspace{14mu}{database}\mspace{14mu}{customers}\mspace{14mu}{with}\mspace{14mu} 1\mspace{14mu}{or}}\mspace{14mu}} \\{{more}\mspace{14mu}{transactions}\mspace{14mu}{at}\mspace{14mu} a\mspace{14mu}{merchant}}\end{matrix}}};$

-   -   or may be more specifically calculated as follows:

${{MINS} = \frac{{\sum\limits_{i = 1}^{I}1}:{\left( {{Ci},{Mj}} \right) \in {{Tint}/{{Size}({Cint})}}}}{{\sum\limits_{i = 1}^{I}1}:{\left( {{Ci},{Mj}} \right) \in {{Ta}/{{Size}({Ca})}}}}},$where Ci is Specific Consumer i, Cint is the set of Interest Consumers,Ca is the set of all Consumers, Mj is Specific Merchant j, Tint is theset of all Interest Transactions, Ta is the set of All Transactions.(Ci, Mj) represents a transaction of Customer i at Merchant j. Cint isthe set of all interest customers, and Ca is the set of all customers.

The MINS of a merchant can be used to filter the merchant list based onvarious criteria. The determination of whether a MINS is sufficient forselection can vary by topic. In various embodiments, the selection maybe a MINS threshold value, an industry type, a combination thereof,and/or any other suitable threshold selection. By way of example andwith reference to FIG. 6C, an overall MINS threshold value may be set at29.1. All the merchants with a MINS value exceeding 29.1 are selectedregardless of the industry type. Further, if the merchant is part of anappropriate industry type, then acceptable MINS value may be lower forthe merchant associated with the appropriate industry type. For example,if the merchant's industry type is related to sports and the MINS valueis at least 27.1, then all merchants matching both criteria (e.g.,sports and a MINS value of at least 27.1) are selected. Merchants withan associated appropriate industry type (e.g., sports) and a MINS valueof less than 27.1 are eliminated. As such, the system may be configuredto provide tiered filtering based on various factors. In this way, amerchant with several relevant factors may be included, even though thestrength or value of a particular factor may be lower than a thresholdfor an individual factor.

In various embodiments and with reference to FIG. 6D, a final merchantscore can be calculated for the filtered merchant list. The finalmerchant score may be a sum of the preliminary merchant score and anormalized MINS. An example of final merchant scores is provided in FIG.6D.

After various calculations of the analysis cycle, the interest merchantgroup is determined by a combination of three groupings. First, a listof initial merchants is retrieved from a merchant database, where amerchant is typically included in the results due to keywordassociation. Merchants with a low final merchant score may be removedfrom the list. Second, small merchants that were removed from theinitial merchant list may be reinstated if the small merchants havesufficient consumer activity. Third, merchants from the consumer boostermerchant list may be included. The merchants on the consumer boostermerchant list have sufficient connectivity to specific, topic-relatedconsumers to be deemed an interest merchant.

In addition to merchant scoring, the consumers used in the analysiscycle may also be scored. In accordance with various embodiments andwith reference to FIG. 7A, a consumer score provides insight intoconsumers that are active in a specific topic based on transactionsassociated with the consumers. In various embodiments, a consumer scoremeasures consumer spend activity among interest merchants combined withan average MINS per consumer. The consumer spend activity calculates thenumber of distinct interest merchants where the consumer has conducted atransaction. A specific time period of activity may be selected tocontrol the scope of the analysis. One exemplary determination of thenumber of distinct interest merchants may be calculated as follows:CDMi=Σ _(i=1) ^(I)1:(Ci,Mj)ϵTint,where Ci is Specific Consumer I, Mj is Specific Merchant j, and Tint isSet of Interest Transactions.

Similarly, the average MINS per consumer may be calculated as follows:

${{Avg}\mspace{14mu}{MINScusti}} = \frac{\sum\limits_{i = 1}^{I}{{dist}\left( {{MINSse} \in {CUSTiROCs}} \right)}}{\sum{distSECusti}}$where CUSTiROCs are the set of Consumer i's transactions; MINSse is amerchant interest network score for interest merchants, and distSECustiis the number of distinct interest merchants shopped at by a customer.

After the consumer score is calculated by summing the consumer spendactivity and the average MINS, the consumer score may be compared to aconsumer threshold value. The consumer is removed from the boostconsumer list if the consumer score is less than the consumer thresholdvalue. In one example, the consumer threshold value may be set at 0.85.FIG. 7B provides an example of consumer scoring, and illustrates howconsumer scoring can distinguish between a ‘baseball enthusiast’ and‘baseball non-enthusiast.’

More specifically, and with reference to FIG. 7B, the threshold valueestablishes a benchmark that allows consumers to be ranked. Where theconsumer is above the threshold, the consumer is considered a ‘baseballenthusiast.’ Where the consumer is below the threshold, the consumer isconsidered a ‘baseball non-enthusiast.’ This delineation or grouping ofconsumer allows the system to tailor promotions and or offer to specificgroups or population (e.g., ‘baseball enthusiast’ and ‘baseballnon-enthusiast’), or use the activities of a group or population tounderstand the interests, spending patterns, and other behaviors of thegroup (e.g., ‘baseball enthusiast’ and ‘baseball non-enthusiast’) orconsumers that may be placed in the group based on future spendingactivity or other behaviors. For example, the system may tailor offersbased on a specific category (e.g., baseball) or spending or activityfor an identified group. The system may identify and provide offers tothe group by any suitable method, including, for example, the system andmethods described in U.S. patent application Ser. No. 12/857,389, filed,Aug. 16, 2010, and entitled System and Method for E-Mail Based Rewards,which is incorporated herein by reference in its entirety

In various embodiments and with reference to FIG. 8, the analysis cyclecan be repeated multiple times until a predetermined limitation isreached. The predetermined limitation may be a set number of iterationsto be completed. In various embodiments, the predetermined limitationmay be reached once a turnover index falls below a specific threshold.In various embodiments, turnover index=(number of merchants added+numberof merchants dropped)/number of merchants starting in the cycle. Theturnover index indicates the impact of another cycle on the interestmerchant list. A lower turnover index value implies that undergoinganother cycle in the process is likely to have a minimal impact on theinterest merchant list.

The threshold associated with the turnover index may be any suitablevalue established by the system. The threshold associated with theturnover index may also be established as a function of the change ofthe turnover index between iterations. For example, where the percentageof changes or one or more iterations is less than a predetermined value(e.g., 0.050) or a percentage changes (e.g., 20%), the system mayautomatically indicate that the turnover index is sufficiently low. Thisindication that the turnover index is sufficiently low indicates thatall or the vast majority of interest merchants have been identified. Forexample, in the example illustrated in FIG. 8, as changes to theturnover index become turnover index decreases over seven cycles, untilit reaches 0.042 (e.g., below a predetermined value such as, forexample, 0.050).

In various embodiments and as discussed herein, a SEED Customer may beused to one or more interest merchants. Information or characteristicsof a customer may be used to determine an appropriate SEED Customer fora selected interest. The system may analyze demographic informationabout a customer, social data associated with a customer, transactioninformation associated with a customer, or any other suitableinformation associated with a customer may be used to select a customeras a SEED Customer. For example, an interest such as, for example,“20-something restaurants” may be identified. Based on the interest, thesystem may analyze any suitable data (e.g., social data, transactiondata, demographic data, and/or the like) associated with customers. Thesystem may identify one of more customer as SEED customers based on thedata associated with the customers. Based on the one or more SEEDCustomers, the system may identify transactions with merchants thatwould be associated with the interest. Those merchant could be analyzedas discussed herein, to create a list or pool of interest merchants thatwould be suitably recommendable for the identified interest and theidentified customer group (e.g., 20-something customers).

Merchants often desire to select specific consumers for providingoffers, as providing offers that are relevant to consumers may result ina higher rate of response. In various embodiments, an assessment of anoffer's relevance to a consumer may be determined or adjusted based on aselection of the consumer. For example, the previously describedanalysis to determine interest enthusiasts and interest merchants is onemanner of determining the relevance of specific merchants to specificconsumers. Typically, recommendation processes and methods are designedto inform a consumer of offers or merchants that may be of interestbased on prior transactions and user information. However, there may betimes where a user is looking for a merchant out of the norm. Theconsumer may be going to dinner with people having different interests.The consumer may also be looking to try something vastly different.Whatever the case may be, often times a consumer will seekrecommendations for such unfamiliar merchants. In various embodiments,consumer information and general merchant transaction data may beprocessed in order to provide recommendations for various categories, orpersonas. With reference to FIG. 9A, a consumer may requestrecommendations for family-friendly merchants, or may requestrecommendations that fit a “Rock Star” persona.

In various embodiments, certain account holder attributes and merchantattributes may be used to determine the “persona” of a merchant. Forexample, the systems and methods, described in greater detail below, maybe configured to capture and characterize consumer data in one or morepersonas. For example, demographic data or transaction information of aconsumer may be initially analyzed to determine what consumer fitpredefined personas. The predefined personas may be any lifestyle,interest, hobby, activity, and/or the like. These initial consumers maybe identified as the interest consumers used to identify interestmerchants as discussed above. Spending patterns and other behaviors ofthe interest consumers may be used to initial create and then refine anassociated list of merchants for each persona.

Persona may be defined in any suitable fashion. Personas may bepredetermined based on known demographic data, based on spend data frominterest consumers, or based on a combination of interest consumer dataand predetermined data. For example, a “Hipster” persona may includedemographic data typically associated with a hipster that may be furtheraugmented or adjusted based on spend data associated with interestconsumers. The interest consumers may be selected and/or associated witha particular persona based on the predefined demographic data ortransaction information that indicates that the interest consumer couldif the Hipster persona.

Once the persona has been created and populated with associatedmerchants that are relevant to the persona, the persona may be storedsuch that it is searchable, presentable, and/or selectable by aconsumer. The consumer selecting the persona may be any suitableconsumer. Typically, the consumer will be a non-interest consumer thatis seeking a recommendation for an item that the consumer would notnormally seek. By selecting a particular persona (e.g., a Hipsterpersona), the consumer may be presenting with recommendations forvarious items (e.g., restaurants, leisure activities, goods, services,and/or the like). The recommendations may be tailored based onpredetermined inputs or preferences (e.g., a search that lead to theinitial persona selection) or dynamically determined inputs (e.g.,inputs from the consumer in real-time and after the initial selection ofthe persona).

Based on these selections and inputs, the system providesrecommendations for various merchants that have been previouslyassociated with the persona. For example and with reference to FIG. 9B,the Hipster persona may result in recommendations for merchants that aretrending upwards in loyalty, have been tagged with “retro” and “clever”,are at a price range of $$$, and are frequented by account holders inthe age range of 19-34. Similarly, a “Starving Artist” persona mayresult in recommendations for merchants that are trending upwards inloyalty, tagged with “good value”, are at a price range of $ or $$, andare frequented by account holders with an income less than $100,000 orother range. The trending characteristics helps differentiate betweendata that is recent versus data that is 6-12 months old or older.

As a part of persona recommendations, a consumer may be presented notonly with a merchant that fits a persona, such as a restaurant, but alsowith various types of merchants that meet the same persona. In variousembodiments and with reference to FIG. 10A-10B, a persona categorizationmethod may include generating a list of consumers who meet a certaininterest topic, such as yoga. The method may include finding a list ofrestaurants at which these selected consumers have dined, andcalculating what percentage of each restaurant's consumers are theselected account holders (e.g., yoga enthusiasts). Further, the methodmay include removing various restaurants from the list that do not meeta threshold for total number of selected consumers, rank ordering therestaurants by percentage of selected consumers (e.g., yogaenthusiasts), and generating a final list of restaurants categorized bythe certain interest. In addition to restaurants, the method may also beapplied to retailers and other merchants frequented by a sufficientnumber and percentage of consumers to qualify as having a “persona”.Moreover, a merchant may have more than one persona, depending on theoverall consumer type.

As described herein, recommendations or offers may be ranked using anysuitable factors. For example, a persona may comprise a plurality ofrecommendations. These recommendations may be ranked and/or presented toa consumer using any suitable factors, based on the channel beingaccessed by the consumer. The channel may also contribute through theranking. For example, the system presenting the personas may alsomonitor activity of a user associated with the channel, such that thesystem understands that the particular consumer's tendencies in responseto the consumer accessing the channel. For example, if a thresholdpercentage (e.g., 90%) of consumer activity in a particular channel isrelated to a particular item (e.g., restaurant reconditions, “buyone/get one” offers, item discounts, and/or the like), the system mayadjust the ranking of identified offers for a particular persona basedon the channel.

The system may also consider whether a restaurant recommendationincludes an associated reward offer or discount. For example, the systemmay identify for recommendation 20 restaurants associated with aparticular persona. The system may then identify that a certain numberof those restaurants also offer a discount that can be associated withthe recommendation. The system may rank recommendations with associatedoffers higher than recommendations without offers, to encourage acustomer to use the system or take advantage of a particular offer.

The system may also rank or sort recommendations based on the sponsoringentity. For example, if a particular merchant is associated with orpartnered with the system, the system may elevate the ranking of arecommendation for the partner merchant. Similarly, the system mayconsider whether an offer is sponsored by a merchant or sponsored by anentity associated with the system and rank the offer based on thatfactor accordingly. The system may also consider a reward associatedwith the offer as part of the ranking of various offers. For example,the system may identify that a certain subset of offers include a rewardof loyalty points and others include a reward of a discount (e.g., astatement credit, authorization credit, instant rebate, discount, POSdiscount and/or the like). If the consumer is not a loyalty accountmember, the system may rank offers that provide a discount, higher thanoffers that provide loyalty point rewards. The system may also rankoffers that provide loyalty points higher, to encourage the consumer tojoin the loyalty program.

In various embodiments of recommendation processes and operations,social data may be used to determine how an item is displayed to aconsumer or to modify a predetermined ranking. Such data may be used toadjust or revise persona recommendations as previously described. Thesocial data may be incorporated to generate a more robust, timely“persona” to include in the persona recommendations. Furthermore, inaccordance with various embodiments, social data may also beincorporated into recommendation engines for generating recommendationsfor a consumer based on the consumer's transaction data and/or socialdata. In various embodiments, social data alone may be sufficient togenerate specific consumer recommendations.

For example, in various embodiments, an offer presented through FACEBOOKmay comprise or be associated with criteria (e.g., keywords, metadata,and/or the like). Social data from FACEBOOK may be captured and comparedto the criteria. Where there is a match between the criteria and thesocial data, the offer may be ranked higher or be displayed moreprominently based on the match. Business rules may be employed to definehow a match is determined. The business rules may require that thesocial data partially match the criteria. The business rules may requirethat the social data exactly match the criteria. An exact match may berequired to adjust the ranking of an offer (where the offer waspreviously ranked based on other data associated with the consumer) toensure that the social data adjusts the ranking in a manner that isrelevant and desirable for the consumer.

For example, company A may have a FACEBOOK page that a consumer can“like.” Another party may also have a FACEBOOK page that is critical ofcompany A that a consumer can “like.” If the consumer “likes” companyA's FACEBOOK page that social data may be used to promote or adjust therankings of an offer from company A based on the direct match. However,if the consumer “likes” the FACEBOOK page that is critical of company A,the social data may be ignored with respect to the ranking of an offerfrom company A or may be used to lower the ranking of the offer fromcompany A.

Rankings of offers may also be adjusted based on other types of socialdata such as consumer broadcasts. For example, a consumer using TWITTERmay broadcast tweets comprising hashtags or particular keywords (e.g.,baseball without a hashtag or “#”). The hashtag or keyword data may becaptured and used to adjust offers associated with the hashtag orkeyword. The hashtag or keyword may be compared to criteria associatedwith the offer. The hashtag or keyword may also be evaluated to identifya consumer's interest, hobby, or preference. This knowledge of theconsumer may affect the ranking of particular offers associated with theinterest, hobby, or preference (e.g., if an offer is relevant to aparticular consumer, the ranking of the offer may be increased).

Accordingly, and with reference to FIG. 11, an exemplary system 1100 fortailoring or recommending an item (e.g., an offer, a merchant, arestaurant, etc.) to a consumer is disclosed. In various embodiments,system 1100 may comprise an item database 1102, an item eligibilitysystem 1104, a scoring system 1106, a real time system 1108, network1110, a web client 1112, a fulfillment system 1114, and/or a merchantsystem 1116.

An item database 1102 may comprise hardware and/or software capable ofstoring data. For example, an item database 102 may comprise a serverappliance running a suitable server operating system (e.g., MICROSOFTINTERNET INFORMATION SERVICES or, “IIS”) and having database software(e.g., ORACLE) installed thereon. In various embodiments, an itemdatabase 1102 may store one or more items, such as one or more offers,associated, for example, with one or more merchants, informationassociated with one or more merchants, and the like.

An item eligibility system 1104 may comprise hardware and/or softwarecapable of determining whether a consumer is eligible to receive an itemand/or information related to an item (e.g., an offer, informationassociated with a merchant). For example, in various embodiments, anitem eligibility system 1104 may determine that a consumer is ineligibleto receive an offer based upon a partnership and/or an affiliationassociated with a transaction account of the consumer (e.g., an AMERICANEXPRESS DELTA SKYMILES consumer may be ineligible to receive an offer onan airline that is not DELTA AIRLINES).

A scoring system 1106 may comprise hardware and/or software capable ofscoring an item. For example, in various embodiments (as describedherein), a scoring system 1106 may analyze a variety of consumerinformation to score an item, such as an offer and/or a merchant.Moreover, in certain embodiments scoring system 1106 may comprise avariety of “closed loop” or internal data associated with a consumer(e.g., as described herein).

A real time system 1108 may comprise hardware and/or software capable ofadjusting the relevance of an item (e.g., a scored offer and/ormerchant) based upon a variety of criteria, such as one or more merchantcriteria, one or more business rules, and the like. For example, asdescribed herein, a real time system 1108 may adjust the relevance of anoffer based upon a merchant interest in acquiring new consumers, amerchant interest in rewarding loyal consumers, a holiday, a particulartime of day, that the consumer is traveling, that the offer isassociated with a merchant who is some distance away from and/or near tothe consumer's location, that the consumer has indicated a preferencenot to receive an offer (e.g., the consumer has given the offer a“thumbs down”), and the like.

A network 1110 may include any electronic communications system ormethod which incorporates hardware and/or software components (e.g. a“cloud” or “cloud computing” system, as described herein). Communicationamong parties via network 1110 may be accomplished through any suitablecommunication channels, such as, for example, a telephone network, anextranet, an intranet, Internet, point of interaction device (point ofsale device, personal digital assistant (e.g., IPHONE, PALM PILOT,BLACKBERRY cellular phone, kiosk, etc.), online communications,satellite communications, off-line communications, wirelesscommunications, transponder communications, local area network (LAN),wide area network (WAN), virtual private network (VPN), networked orlinked devices, keyboard, mouse and/or any suitable communication ordata input modality. Moreover, although the system 1100 is frequentlydescribed herein as being implemented with TCP/IP communicationsprotocols, the system may also be implemented using IPX, APPLETALK,IP-6, NetBIOS, OSI, any tunneling protocol (e.g. IPsec, SSH), or anynumber of existing or future protocols. If network 1110 is in the natureof a public network, such as the Internet, it may be advantageous topresume network 1110 to be insecure and open to eavesdroppers. Specificinformation related to the protocols, standards, and applicationsoftware utilized in connection with the Internet is generally known tothose skilled in the art and, as such, need not be detailed herein. See,for example, Dilip Naik, Internet Standards and Protocols (1998); Java 2Complete, various authors, (Sybex 1999); Deborah Ray and Eric Ray,Mastering HTML 4.0 (1997); and Loshin, TCP/IP Clearly Explained (1997)and David Gourley and Brian Totty, HTTP, The Definitive Guide (2002),the contents of which are hereby incorporated by reference.

The various system components may be independently and separately orcollectively suitably coupled to network 1110 via data links whichinclude, for example, a connection to an Internet Service Provider (ISP)over the local loop as is typically used in connection with standardmodem communication, cable modem, Dish networks, ISDN, DigitalSubscriber Line (DSL), or various wireless communication methods, see,e.g., Gilbert Held, Understanding Data Communications (1996), which ishereby incorporated by reference. It is noted that network 1110 may beimplemented variously, such as, for example, as an interactivetelevision (ITV) network. Moreover, this disclosure contemplates theuse, sale or distribution of any goods, services or information over anynetwork having similar functionality described herein.

As used herein, a “cloud” or “cloud computing” may describe a model forenabling convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services) that can be rapidly provisioned and releasedwith minimal management effort or service provider interaction. Cloudcomputing may include location-independent computing, whereby sharedservers provide resources, software, and data to computers and otherdevices on demand. For more information regarding cloud computing, seethe NIST's (National Institute of Standards and Technology) definitionof cloud computing athttp://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (lastvisited June 2012), which is hereby incorporated by reference in itsentirety.

Web client 1112 may include any device (e.g., a personal computer, amobile communications device, and the like) which communicates via anynetwork, for example such as those discussed herein. Web client 1112 mayinclude one or more browsers or browser applications and/or applicationprograms, including browser applications comprising Internet browsingsoftware installed within a computing unit or a system to conduct onlinetransactions and/or communications. For example, in various embodiments,web client 1112 may include (and run) MICROSOFT INTERNET EXPLORER,MOZILLA FIREFOX, GOOGLE CHROME, APPLE SAFARI, and/or any softwarepackage available for browsing the Internet.

A computing unit or system may take the form of a computer or set ofcomputers, although other types of computing units or systems may beused, including tablets, laptops, notebooks, hand held computers,personal digital assistants, cellular phones, smart phones, set-topboxes, workstations, computer-servers, main frame computers,mini-computers, PC servers, pervasive computers, network sets ofcomputers, personal computers, such as IPADs, IMACs, and MACBOOKS,kiosks, terminals, point of sale (POS) devices and/or terminals,televisions, GPS receivers, in-dash vehicle displays, and/or any otherdevice capable of receiving data over a network. The computing unit ofthe web client 1112 may be further equipped with an Internet browserconnected to the Internet or an intranet using standard dial-up, cable,DSL or any other Internet protocol known in the art. Transactionsoriginating at a web client 1112 may pass through a firewall in order toprevent unauthorized access from users of other networks. Further,additional firewalls may be deployed between the varying components ofsystem 1100 to further enhance security.

In various embodiments, web client 1112 may or may not be in directcontact with an application server. For example, web client 1112 mayaccess the services of an application server through another serverand/or hardware component, which may have a direct or indirectconnection to an Internet server. For example, web client 1112 maycommunicate with an application server via a load balancer and/or a webserver. In an exemplary embodiment, access is through a network or theInternet through a commercially-available web-browser software package.

Web client 1112 may further include an operating system (e.g., WINDOWSNT/95/98/2000/XP/VISTA/7/8/CE/MOBILE, OS2, UNIX, Linux, SOLARIS, MACOS,PALMOS, etc.) as well as various conventional support software anddrivers typically associated with computers. Web client 1112 may be in ahome or business environment with access to a network. Web client 1112may implement security protocols such as Secure Sockets Layer (SSL) andTransport Layer Security (TLS). Web client 1112 may further implementseveral application layer protocols including http, https, ftp, andsftp.

A fulfillment system 1114 may comprise any hardware and/or softwarecapable of fulfilling and/or facilitating the fulfillment. For instance,a fulfillment system 1114 may, in various embodiments, comprise hardwareand/or software capable of fulfilling and/or facilitating fulfillment ofan offer. In various embodiments, a fulfillment system 1114 may comprisea system, such as a system described in U.S. patent application Ser. No.11/779,734, filed, Jul. 18, 2007, and entitled Loyalty Incentive ProgramUsing Transaction Cards, which is incorporated herein by reference inits entirety. In addition, in various embodiments, a fulfillment system1114 may comprise a system, such as a system described in U.S. patentapplication Ser. No. 12/857,389, filed, Aug. 16, 2010, and entitledSystem and Method for E-Mail Based Rewards, which is incorporated hereinby reference in its entirety. In various embodiments, a fulfillmentsystem 1114 may comprise a system, such as a system described in U.S.patent application Ser. No. 13/153,890, filed, Jun. 6, 2011, andentitled System and Method for Administering Marketing Programs, whichis incorporated herein by reference in its entirety. In variousembodiments, a fulfillment system 1114 may comprise a system, such as asystem described in U.S. patent application Ser. No. 13/021,327, filed,Feb. 4, 2011, and entitled Systems and Methods for Providing LocationBased Coupon-Less Offers to Registered Account holders, which isincorporated herein by reference in its entirety. In variousembodiments, a fulfillment system 1114 may comprise a system, such as asystem described in U.S. patent application Ser. No. 13/153,890, filed,Jun. 6, 2011, and entitled System and Method for Administering MarketingPrograms, which is incorporated herein by reference in its entirety. Invarious embodiments, a fulfillment system 114 may comprise a system,such as a system described in U.S. patent application Ser. No.13/188,693, filed, Jul. 22, 2011, and entitled System and Method forCoupon-Less Product Level Discounts, which is incorporated herein byreference in its entirety. In various embodiments, a fulfillment system1114 may comprise a system, such as a system described in U.S. patentapplication Ser. No. 13/411,281, filed, Mar. 2, 2012, and entitledSystem and Method for Providing Coupon-Less Discounts Based on a UserBroadcasted Message, which is incorporated herein by reference in itsentirety. In various embodiments, a fulfillment system 1114 may comprisea system, such as a system described in U.S. patent application Ser. No.13/439,768, filed, Apr. 4, 2012, and entitled System and Method forProviding International Coupon-Less Discounts, which is incorporatedherein by reference in its entirety.

A merchant reporting system 1116 may comprise any hardware and/orsoftware capable of generating a report and/or providing a report to amerchant. For example, in various embodiments, a merchant reportingsystem 1116 may generate a report illustrating a ROI received by themerchant as the result of a tailored marketing campaign.

In various embodiments, the recommendations and/or interests may beconfigured as an input of an output. For example, the recommendationsand/or interests may be configured as initial inputs to a system that isconfigured to provide specific tailored offers for individual customers,consumers, members, users and/or the like, including the systemdescribed in U.S. Provisional Application 61/646,778 filed on May 14,2012 and entitled “SYSTEMS AND METHODS FOR TAILORED MARKETING BASED ONFILTERING,” (hereinafter, the “'778 Provisional”) which is hereinincorporated by reference in its entirety. The specific tailored offersand/or recommendations produced by an appropriate system may also beinputs or SEED Merchants for a recommender system, such as the systemdescribed in '778 Provisional.

In various embodiments and with reference to FIG. 12, a process 1200 fortailoring and/or recommending a relevant item, such as an offer and/ormerchant, to a consumer is described. In addition to the processesdescribed below, in various embodiments, a process for tailoringmarketing to a consumer as described in U.S. patent application Ser. No.13/245,636, filed, Sep. 26, 2011, and entitled Systems and Methods forTargeting Ad Impressions, which is incorporated herein by reference inits entirety, may be implemented. Similarly, in various embodiments, aprocess for tailoring marketing to a consumer as described in U.S.patent application Ser. No. 13/172,676, filed, Jun. 29, 2011, andentitled Spend Based Digital Ad Targeting and Measurement, which isincorporated herein by reference in its entirety, may be implemented.Further, in various embodiments, a process for tailoring marketing to aconsumer as described in U.S. patent application Ser. No. 13/348,432,filed, Jan. 11, 2012, and entitled Systems and Methods for Digital SpendBased Targeting and Measurement, which is incorporated herein byreference in its entirety, may be implemented.

Further still, in various embodiments and in addition to the processesdescribed below, a process for tailoring marketing to a consumer asdescribed in U.S. patent application Ser. No. 11/315,262, filed, Dec.23, 2005, and entitled Method and Apparatus for Collaborative Filteringof Account holder Transactions, which is incorporated herein byreference in its entirety, may be implemented. In addition, in variousembodiments a process for tailoring marketing to a consumer as describedin U.S. patent application Ser. No. 11/500,492, filed, Aug. 8, 2006, andentitled System and Method for Predicting Account holder Spending UsingCollaborative Filtering, which is incorporated herein by reference inits entirety, may be implemented.

Further still, in various embodiments and in addition to the processesdescribed below, a process for tailoring marketing to a consumer asdescribed in U.S. Provisional Patent Application Ser. No. 61/610,461,filed, Mar. 13, 2012, and entitled Generating Merchant Recommendationsfor Consumers, which is incorporated herein by reference in itsentirety, may be implemented. In addition, in various embodiments aprocess for tailoring marketing to a consumer as described in U.S.Provisional Patent Application Ser. No. 61/610,981, filed, Mar. 14,2012, and entitled Generating a Consumer Review Using Customized Tags,which is incorporated herein by reference in its entirety, may beimplemented. Moreover, in various embodiments a process for tailoringmarketing to a consumer as described in U.S. Provisional PatentApplication Ser. No. 61/610,983, filed, Mar. 14, 2012, and entitledTransaction Rewards List, which is incorporated herein by reference inits entirety, may be implemented.

Referring broadly to FIG. 12, each of the steps 1202-1206 may beperformed alone and/or in combination with any other step 1202-1206.Accordingly, as shown, in various embodiments, a consumer's eligibilitymay be assessed (step 1202). As described herein, an item eligibilitysystem 104 may assess a consumer's eligibility to receive an item, suchas an offer and/or information (e.g., a recommendation) associated witha merchant. Furthermore, a consumer may be ineligible to receive an itemfor many reasons, but to give one example, a consumer may be ineligibleto receive an item, because the consumer holds a branded or partnertransaction account (e.g., an AMERICAN EXPRESS DELTA SKYMILES account)that is associated with a partner and/or merchant (e.g., DELTA AIRLINES)that is unaffiliated with (and/or a competitor of) an item associatedwith another merchant (e.g., SOUTHWEST AIRLINES).

Further, in various embodiments, a consumer relevance value or score maybe determined, as described herein (e.g., by a scoring system 1106)(step 1204). For instance, in various embodiments, a consumer relevancevalue (or “CRV”) may be determined based on content and/or an industryassociated with one or more items. Similarly, in various embodiments, aconsumer relevance value may be determined based on a collaborativefiltering algorithm, as described herein. Further, in variousembodiments, a consumer relevance value may be based on either or bothof content and/or a collaborative filtering algorithm.

Further still, in various embodiments, a consumer relevance valueassociated with an item such as an offer and/or merchant may be adjustedand/or determined based on one or more merchant goals and/or one or morebusiness rules (e.g., by a real time system 1108) (step 1206). Forexample, as described herein, a consumer relevance value may be adjustedbased on a merchant goal to acquire only new consumers, tailor existingconsumers of the merchant, and/or tailor all consumers. Equally, invarious embodiments, a consumer relevance value may be adjusted based ona holiday, a particular time of day, a determination that the consumeris traveling, a determination that the item is associated with amerchant who is some distance away and/or near to from the consumer'slocation, because the consumer has indicated a preference not to receivethe item (e.g., the consumer has given the offer a “thumbs down”), andthe like.

The phrases consumer, customer, user, account holder, account affiliate,cardmember or the like may be used interchangeably and shall include anyperson, group, entity, business, organization, business, software,hardware, machine and/or combination of these, and may, in variousembodiments, be associated with a transaction account, buy merchantofferings offered by one or more merchants using the account and/or belegally designated for performing transactions on the account,regardless of whether a physical card is associated with the account.For example, a consumer or account affiliate may include a transactionaccount owner, a transaction account user, an account affiliate, a childaccount user, a subsidiary account user, a beneficiary of an account, acustodian of an account, and/or any other person or entity affiliated orassociated with a transaction account.

In various embodiments, a consumer may receive a tailored offer, asdescribed below. A bank may be part of the system, but the bank mayrepresent other types of card issuing institutions, such as credit cardcompanies, card sponsoring companies, or third party issuers undercontract with financial institutions. It is further noted that otherparticipants may be involved in some phases of the transaction, such asan intermediary settlement institution, but these participants are notshown.

Phrases and terms similar to “business,” “merchant,” “serviceestablishment,” or “SE” may be used interchangeably with each other andshall mean any person, entity, distributor system, software and/orhardware that is a provider, broker and/or any other entity in thedistribution chain of goods or services. For example, a merchant may bea grocery store, a retail store, a restaurant, a travel agency, aservice provider, an on-line merchant and/or the like. In variousembodiments, a merchant may request payment for goods sold to a consumeror consumer who holds an account with a transaction account issuer.

As used herein, terms such as “transmit,” “communicate” and/or “deliver”may include sending electronic data from one system component to anotherover a network connection. Additionally, as used herein, “data” mayinclude information such as commands, queries, files, data for storage,and the like in digital or any other form.

Phrases and terms similar to “item” may include any good, service,offer, merchant, type of merchant, demographic data, consumer profiledata, consumer profile, type of transaction account, transactionaccount, period of time (e.g., a period of time a consumer has been aconsumer of a transaction account issuer), size of wallet, share ofwallet, information, and the like. Further, in various embodiments, anitem may comprise an output or result of a collaborative filteringalgorithm, an algorithm for matching a consumer with a particularinterest or set of interests (e.g., baseball or yoga, to provide a fewexamples), and the like. In various embodiments, such an algorithm maybe referred to as an “interest graph” or an “interest graphingalgorithm.”

As used herein, an “offer” may comprise any data and/or information. Asdescribed herein, an offer may comprise one or more items. In addition,an offer may comprise data associated with one or more items, also asdiscussed herein. An offer may further comprise one or morecharacteristics or metadata. The characteristics or metadata associatedwith an offer may describe one or more attributes associated with theoffer. Further, in various embodiments, an offer may comprise an offerto purchase good or service offered for sale by a merchant or SE.Similarly, in various embodiments, an offer may be associated with amerchant or SE.

As used herein, “record of charge” or “ROC” may comprise a record of atransaction or charge by a consumer with a particular merchant. Invarious embodiments, a ROC may comprise a cumulative value, which mayindicate a number of total transactions or purchases a consumer has madefrom a particular merchant. Further, in various embodiments, a ROC maysimply comprise an indication that a consumer has made at least onepurchase from a merchant (e.g., within a particular time period). Forinstance, where a consumer's transaction history shows that the consumerhas made at least one purchase from a merchant (e.g., within 12 months),a ROC may simply comprise a binary value, such as a “1” or a “yes.”Conversely, where a consumer's transaction history indicates that aconsumer has not made a purchase from a merchant, a ROC may simplycomprise a value such as a “0” or a “no”.

A “channel” may include any system or method for delivering content,and/or the content itself. The content may be presented in any form ormedium, and in various embodiments, the content may be deliveredelectronically and/or capable of being presented electronically. Forexample, a channel may comprise a website, a uniform resource locator(“URL”), a document (e.g., a Microsoft Word document, a Microsoft Exceldocument, an Adobe pdf document, etc.), an “ebook,” an “emagazine,” anapplication or micro-application (as described below), a text message,an email, and the like. In various embodiments, a channel may be hostedor provided by a data partner. Further, in various embodiments, achannel may comprise a social media channel, such as FACEBOOK,FOURSQUARE, TWITTER, and the like.

A “consumer profile” or “consumer profile data” may comprise anyinformation or data about a consumer that describes an attributeassociated with the consumer (e.g., a preference, an interest,demographic information, personally identifying information, and thelike).

In various embodiments, a consumer profile may be based upon a varietyof data. For example, a consumer profile may be based upon data that isreceived, culled, collected, and/or derived from a variety of sources,such as a consumer's transaction history, data associated with oravailable via a consumer's social networking profile (e.g., a consumer'sFACEBOOK profile), data associated with a consumer's physical location,and/or other publicly and/or privately available sources of informationabout a consumer. In various embodiments, a consumer profile may not bebased upon such data, unless a consumer opts in or requests that suchdata be used.

Further, in various embodiments, a consumer profile may be based upondata contributed by a consumer, a merchant, a third party, and/or an SE(as described below). Such data may comprise, for example, a consumer'spersonal information, e.g., demographic information, a consumer's dateof birth, a consumer's residence information, an address of theconsumer's work, a specific preference associated with the consumer(e.g., a preference for a certain type of vacation, such as a preferencefor a tropical vacation), a website in which the consumer is interested,and the like. Further, a consumer may contribute data towards a consumerprofile by way of a form and/or questionnaire, such as, for example, aweb-based form or questionnaire.

With further regard to the types of data which may be contributed to aconsumer profile, in general, any information that a consumer would liketo serve as a basis for a consumer profile may be contributed. Forinstance, a consumer profile may comprise location data (e.g., dataassociated with a global positioning system, a home address, a workaddress, family location data, data about a consumer's most shopped orfavorite shopping locations, data about a consumer's most visited orfavorite places), data associated with a consumer's favorite websites,digital destinations, or magazines (e.g., blogs, news websites, shoppingwebsites, research websites, financial websites, etc.), personal data(e.g., email addresses, physical addresses, phone numbers, ageinformation, income information, expenses information, etc.), dataassociated with a consumer's status or mode of travel (e.g., vacationdata, business data, personal data, airline data, lodging data, etc.),data associated with a consumer's favorite items (e.g., food,restaurants, groceries, electronics, music, gaming, clothing types,hobbies, fitness, etc.), and the like.

In addition, in various embodiments, a consumer profile may includeonline tracking cookie data, web beacon data, web tracking data, webpacket trace data, digital fingerprint data, clickstream data, purchaseor transaction history data, data entered by a consumer in a web basedform, data purchased by a merchant about a consumer, social networkingdata, banking and/or credit card data, stock keeping unit (“SKU”) data,transactional and/or budget data, coupon data, retail data (e.g., itemspurchased, wish lists, etc.), data from third party personal dataaggregators, search engine data, and/or any other data which themerchant may have in its possession or to which the merchant may gainaccess.

In various embodiments, a consumer may specify that a consumer profilemay be based upon certain data, but that the profile should not be basedupon other data. For example, a consumer may specify that the consumer'sprofile may be based upon data associated with the consumer'stransaction history, but may not be based upon data culled from theconsumer's social networking profile.

Phrases and terms similar to “account,” “transaction account,”“account,” “account number,” “account code,” and/or “consumer account”may include any account that may be used to facilitate a financialtransaction. These accounts may include any device, code (e.g., one ormore of an authorization/access code, personal identification number(“PIN”), Internet code, other identification code, and/or the like),number, letter, symbol, digital certificate, smart chip, digital signal,analog signal, biometric or other identifier/indicia suitably configuredto allow the consumer to access, interact with or communicate with thesystem. The account number may optionally be located on or associatedwith a rewards account, charge account, credit account, debit account,prepaid account, telephone card, embossed card, smart card, magneticstripe card, bar code card, transponder, radio frequency card or anassociated account.

Systems, methods and computer program products are provided. In thedetailed description herein, references to “various embodiments”, “oneembodiment”, “an embodiment”, “an example embodiment”, etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to effect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

In various embodiments, the methods described herein are implementedusing the various particular machines described herein. The methodsdescribed herein may be implemented using the below particular machines,and those hereinafter developed, in any suitable combination, as wouldbe appreciated immediately by one skilled in the art. Further, as isunambiguous from this disclosure, the methods described herein mayresult in various transformations of certain articles.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the system. As those skilledin the art will appreciate, user computer may include an operatingsystem (e.g., Windows NT, Windows 95/98/2000, Windows XP, Windows Vista,Windows 7, OS2, UNIX, Linux, Solaris, MacOS, etc.) as well as variousconventional support software and drivers typically associated withcomputers. A user may include any individual, business, entity,government organization, software and/or hardware that interact with asystem.

In various embodiments, various components, modules, and/or engines ofsystem 100 may be implemented as micro-applications or micro-apps.Micro-apps are typically deployed in the context of a mobile operatingsystem, including for example, a Palm mobile operating system, a Windowsmobile operating system, an Android Operating System, Apple iOS, aBlackberry operating system and the like. The micro-app may beconfigured to leverage the resources of the larger operating system andassociated hardware via a set of predetermined rules which govern theoperations of various operating systems and hardware resources. Forexample, where a micro-app desires to communicate with a device ornetwork other than the mobile device or mobile operating system, themicro-app may leverage the communication protocol of the operatingsystem and associated device hardware under the predetermined rules ofthe mobile operating system. Moreover, where the micro-app desires aninput from a user, the micro-app may be configured to request a responsefrom the operating system which monitors various hardware components andthen communicates a detected input from the hardware to the micro-app.

The system contemplates uses in association with web services, utilitycomputing, pervasive and individualized computing, security and identitysolutions, autonomic computing, cloud computing, commodity computing,mobility and wireless solutions, open source, biometrics, grid computingand/or mesh computing.

Any databases discussed herein may include relational, hierarchical,graphical, or object-oriented structure and/or any other databaseconfigurations. Common database products that may be used to implementthe databases include DB2 by IBM (Armonk, N.Y.), various databaseproducts available from Oracle Corporation (Redwood Shores, Calif.),Microsoft Access or Microsoft SQL Server by Microsoft Corporation(Redmond, Wash.), MySQL by MySQL AB (Uppsala, Sweden), or any othersuitable database product. Moreover, the databases may be organized inany suitable manner, for example, as data tables or lookup tables. Eachrecord may be a single file, a series of files, a linked series of datafields or any other data structure. Association of certain data may beaccomplished through any desired data association technique such asthose known or practiced in the art. For example, the association may beaccomplished either manually or automatically. Automatic associationtechniques may include, for example, a database search, a databasemerge, GREP, AGREP, SQL, using a key field in the tables to speedsearches, sequential searches through all the tables and files, sortingrecords in the file according to a known order to simplify lookup,and/or the like. The association step may be accomplished by a databasemerge function, for example, using a “key field” in pre-selecteddatabases or data sectors. Various database tuning steps arecontemplated to optimize database performance. For example, frequentlyused files such as indexes may be placed on separate file systems toreduce In/Out (“I/O”) bottlenecks.

More particularly, a “key field” partitions the database according tothe high-level class of objects defined by the key field. For example,certain types of data may be designated as a key field in a plurality ofrelated data tables and the data tables may then be linked on the basisof the type of data in the key field. The data corresponding to the keyfield in each of the linked data tables is preferably the same or of thesame type. However, data tables having similar, though not identical,data in the key fields may also be linked by using AGREP, for example.In accordance with one embodiment, any suitable data storage techniquemay be utilized to store data without a standard format. Data sets maybe stored using any suitable technique, including, for example, storingindividual files using an ISO/IEC 7816-4 file structure; implementing adomain whereby a dedicated file is selected that exposes one or moreelementary files containing one or more data sets; using data setsstored in individual files using a hierarchical filing system; data setsstored as records in a single file (including compression, SQLaccessible, hashed via one or more keys, numeric, alphabetical by firsttuple, etc.); Binary Large Object (BLOB); stored as ungrouped dataelements encoded using ISO/IEC 7816-6 data elements; stored as ungroupeddata elements encoded using ISO/IEC Abstract Syntax Notation (ASN.1) asin ISO/IEC 8824 and 8825; and/or other proprietary techniques that mayinclude fractal compression methods, image compression methods, etc.

In various embodiments, the ability to store a wide variety ofinformation in different formats is facilitated by storing theinformation as a BLOB. Thus, any binary information can be stored in astorage space associated with a data set. As discussed above, the binaryinformation may be stored on the financial transaction instrument orexternal to but affiliated with the financial transaction instrument.The BLOB method may store data sets as ungrouped data elements formattedas a block of binary via a fixed memory offset using either fixedstorage allocation, circular queue techniques, or best practices withrespect to memory management (e.g., paged memory, least recently used,etc.). By using BLOB methods, the ability to store various data setsthat have different formats facilitates the storage of data associatedwith the financial transaction instrument by multiple and unrelatedowners of the data sets. For example, a first data set which may bestored may be provided by a first party, a second data set which may bestored may be provided by an unrelated second party, and yet a thirddata set which may be stored, may be provided by an third partyunrelated to the first and second party. Each of these three exemplarydata sets may contain different information that is stored usingdifferent data storage formats and/or techniques. Further, each data setmay contain subsets of data that also may be distinct from other subsets.

As stated above, in various embodiments, the data can be stored withoutregard to a common format. However, in one exemplary embodiment, thedata set (e.g., BLOB) may be annotated in a standard manner whenprovided for manipulating the data onto the financial transactioninstrument. The annotation may comprise a short header, trailer, orother appropriate indicator related to each data set that is configuredto convey information useful in managing the various data sets. Forexample, the annotation may be called a “condition header”, “header”,“trailer”, or “status”, herein, and may comprise an indication of thestatus of the data set or may include an identifier correlated to aspecific issuer or owner of the data. In one example, the first threebytes of each data set BLOB may be configured or configurable toindicate the status of that particular data set; e.g., LOADED,INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequent bytes ofdata may be used to indicate for example, the identity of the issuer,user, transaction/membership account identifier or the like. Each ofthese condition annotations are further discussed herein.

The data set annotation may also be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

The data, including the header or trailer may be received by a standalone interaction device configured to add, delete, modify, or augmentthe data in accordance with the header or trailer. As such, in oneembodiment, the header or trailer is not stored on the transactiondevice along with the associated issuer-owned data but instead theappropriate action may be taken by providing to the transactioninstrument user at the stand alone device, the appropriate option forthe action to be taken. The system may contemplate a data storagearrangement wherein the header or trailer, or header or trailer history,of the data is stored on the transaction instrument in relation to theappropriate data.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of thesystem may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.

Encryption may be performed by way of any of the techniques nowavailable in the art or which may become available—e.g., Twofish, RSA,El Gamal, Schorr signature, DSA, PGP, PKI, and symmetric and asymmetriccryptosystems.

The computing unit of the web client may be further equipped with anInternet browser connected to the Internet or an intranet using standarddial-up, cable, DSL or any other Internet protocol known in the art.Transactions originating at a web client may pass through a firewall inorder to prevent unauthorized access from users of other networks.Further, additional firewalls may be deployed between the varyingcomponents of CMS to further enhance security.

Firewall may include any hardware and/or software suitably configured toprotect CMS components and/or enterprise computing resources from usersof other networks. Further, a firewall may be configured to limit orrestrict access to various systems and components behind the firewallfor web clients connecting through a web server. Firewall may reside invarying configurations including Stateful Inspection, Proxy based,access control lists, and Packet Filtering among others. Firewall may beintegrated within an web server or any other CMS components or mayfurther reside as a separate entity. A firewall may implement networkaddress translation (“NAT”) and/or network address port translation(“NAPT”). A firewall may accommodate various tunneling protocols tofacilitate secure communications, such as those used in virtual privatenetworking. A firewall may implement a demilitarized zone (“DMZ”) tofacilitate communications with a public network such as the Internet. Afirewall may be integrated as software within an Internet server, anyother application server components or may reside within anothercomputing device or may take the form of a standalone hardwarecomponent.

The computers discussed herein may provide a suitable website or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem. In one embodiment, the Apache web server is used in conjunctionwith a Linux operating system, a MySQL database, and the Perl, PHP,and/or Python programming languages.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a website having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that might be used to interact with theuser. For example, a typical website might include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), common gateway interface scripts (CGI), extensiblemarkup language (XML), dynamic HTML, cascading style sheets (CSS), AJAX(Asynchronous Javascript And XML), helper applications, plug-ins, andthe like. A server may include a web service that receives a requestfrom a web server, the request including a URL(http://yahoo.com/stockquotes/ge) and an IP address (123.56.789.234).The web server retrieves the appropriate web pages and sends the data orapplications for the web pages to the IP address. Web services areapplications that are capable of interacting with other applicationsover a communications means, such as the internet. Web services aretypically based on standards or protocols such as XML, SOAP, AJAX, WSDLand UDDI. Web services methods are well known in the art, and arecovered in many standard texts. See, e.g., ALEX NGHIEM, IT WEB SERVICES:A ROADMAP FOR THE ENTERPRISE (2003), hereby incorporated by reference.

Middleware may include any hardware and/or software suitably configuredto facilitate communications and/or process transactions betweendisparate computing systems. Middleware components are commerciallyavailable and known in the art. Middleware may be implemented throughcommercially available hardware and/or software, through custom hardwareand/or software components, or through a combination thereof. Middlewaremay reside in a variety of configurations and may exist as a standalonesystem or may be a software component residing on the Internet server.Middleware may be configured to process transactions between the variouscomponents of an application server and any number of internal orexternal systems for any of the purposes disclosed herein. Web SphereMQTM (formerly MQSeries) by IBM, Inc. (Armonk, N.Y.) is an example of acommercially available middleware product. An Enterprise Service Bus(“ESB”) application is another example of middleware.

Practitioners will also appreciate that there are a number of methodsfor displaying data within a browser-based document. Data may berepresented as standard text or within a fixed list, scrollable list,drop-down list, editable text field, fixed text field, pop-up window,and the like. Likewise, there are a number of methods available formodifying data in a web page such as, for example, free text entry usinga keyboard, selection of menu items, check boxes, option boxes, and thelike.

The system and method may be described herein in terms of functionalblock components, screen shots, optional selections and variousprocessing steps. It should be appreciated that such functional blocksmay be realized by any number of hardware and/or software componentsconfigured to perform the specified functions. For example, the systemmay employ various integrated circuit components, e.g., memory elements,processing elements, logic elements, look-up tables, and the like, whichmay carry out a variety of functions under the control of one or moremicroprocessors or other control devices. Similarly, the softwareelements of the system may be implemented with any programming orscripting language such as C, C++, C#, Java, JavaScript, VBScript,Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly,PERL, PHP, awk, Python, Visual Basic, SQL Stored Procedures, PL/SQL, anyUNIX shell script, and extensible markup language (XML) with the variousalgorithms being implemented with any combination of data structures,objects, processes, routines or other programming elements. Further, itshould be noted that the system may employ any number of conventionaltechniques for data transmission, signaling, data processing, networkcontrol, and the like. Still further, the system could be used to detector prevent security issues with a client-side scripting language, suchas JavaScript, VBScript or the like. For a basic introduction ofcryptography and network security, see any of the following references:(1) “Applied Cryptography: Protocols, Algorithms, And Source Code In C,”by Bruce Schneier, published by John Wiley & Sons (second edition,1995); (2) “Java Cryptography” by Jonathan Knudson, published byO'Reilly & Associates (1998); (3) “Cryptography & Network Security:Principles & Practice” by William Stallings, published by Prentice Hall;all of which are hereby incorporated by reference.

With further regard to terms such as “consumer,” “customer,” “merchant,”and the like, each of these participants may be equipped with acomputing device in order to interact with the system and facilitateonline commerce transactions. A consumer or consumer may have acomputing unit in the form of a personal computer, although other typesof computing units may be used including laptops, notebooks, hand heldcomputers, set-top boxes, cellular telephones, touch-tone telephones andthe like. A merchant may have a computing unit implemented in the formof a computer-server, although other implementations are contemplated bythe system. A bank may have a computing center shown as a main framecomputer. However, the bank computing center may be implemented in otherforms, such as a mini-computer, a PC server, a network of computerslocated in the same of different geographic locations, or the like.Moreover, the system contemplates the use, sale or distribution of anygoods, services or information over any network having similarfunctionality described herein

A merchant computer and/or a bank computer may be interconnected via asecond network, referred to as a payment network. The payment networkwhich may be part of certain transactions represents existingproprietary networks that presently accommodate transactions for creditcards, debit cards, and other types of financial/banking cards. Thepayment network is a closed network that is assumed to be secure fromeavesdroppers. Exemplary transaction networks may include the AmericanExpress®, VisaNet® and the Veriphone® networks.

An electronic commerce system may be implemented at the consumer andissuing bank. In an exemplary implementation, the electronic commercesystem may be implemented as computer software modules loaded onto theconsumer computer and the banking computing center. The merchantcomputer may not require any additional software to participate in theonline commerce transactions supported by the online commerce system.

As will be appreciated by one of ordinary skill in the art, the systemmay be embodied as a customization of an existing system, an add-onproduct, a processing apparatus executing upgraded software, astandalone system, a distributed system, a method, a data processingsystem, a device for data processing, and/or a computer program product.Accordingly, any portion of the system or a module may take the form ofa processing apparatus executing code, an internet based embodiment, anentirely hardware embodiment, or an embodiment combining aspects of theinternet, software and hardware. Furthermore, the system may take theform of a computer program product on a computer-readable storage mediumhaving computer-readable program code means embodied in the storagemedium. Any suitable computer-readable storage medium may be utilized,including hard disks, CD-ROM, optical storage devices, magnetic storagedevices, and/or the like.

The system and method is described herein with reference to screenshots, block diagrams and flowchart illustrations of methods, apparatus(e.g., systems), and computer program products according to variousembodiments. It will be understood that each functional block of theblock diagrams and the flowchart illustrations, and combinations offunctional blocks in the block diagrams and flowchart illustrations,respectively, can be implemented by computer program instructions.

These computer program instructions may be loaded onto a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructionsthat execute on the computer or other programmable data processingapparatus create means for implementing the functions specified in theflowchart block or blocks. These computer program instructions may alsobe stored in a computer-readable memory that can direct a computer orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, webpages, websites, web forms, prompts, etc. Practitionerswill appreciate that the illustrated steps described herein may comprisein any number of configurations including the use of windows, webpages,web forms, popup windows, prompts and the like. It should be furtherappreciated that the multiple steps as illustrated and described may becombined into single webpages and/or windows but have been expanded forthe sake of simplicity. In other cases, steps illustrated and describedas single process steps may be separated into multiple webpages and/orwindows but have been combined for simplicity.

The term “non-transitory” is to be understood to remove only propagatingtransitory signals per se from the claim scope and does not relinquishrights to all standard computer-readable media that are not onlypropagating transitory signals per se. Stated another way, the meaningof the term “non-transitory computer-readable medium” should beconstrued to exclude only those types of transitory computer-readablemedia which were found in In Re Nuijten to fall outside the scope ofpatentable subject matter under 35 U.S.C. § 101.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any elements that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of the disclosure. The scope of the disclosure isaccordingly to be limited by nothing other than the appended claims, inwhich reference to an element in the singular is not intended to mean“one and only one” unless explicitly so stated, but rather “one ormore.” Moreover, where a phrase similar to ‘at least one of A, B, and C’or ‘at least one of A, B, or C’ is used in the claims or specification,it is intended that the phrase be interpreted to mean that A alone maybe present in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the disclosureincludes a method, it is contemplated that it may be embodied ascomputer program instructions on a tangible computer-readable carrier,such as a magnetic or optical memory or a magnetic or optical disk. Allstructural, chemical, and functional equivalents to the elements of theabove-described exemplary embodiments that are known to those ofordinary skill in the art are expressly incorporated herein by referenceand are intended to be encompassed by the present claims. Moreover, itis not necessary for a device or method to address each and everyproblem sought to be solved by the present disclosure, for it to beencompassed by the present claims. Furthermore, no element, component,or method step in the present disclosure is intended to be dedicated tothe public regardless of whether the element, component, or method stepis explicitly recited in the claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. 112, sixth paragraph, unlessthe element is expressly recited using the phrase “means for.” As usedherein, the terms “comprises”, “comprising”, or any other variationthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, article, or apparatus that comprises a list of elementsdoes not include only those elements but may include other elements notexpressly listed or inherent to such process, method, article, orapparatus.

The system may include or interface with any of the foregoing accounts,devices, and/or a transponder and reader (e.g. RFID reader) in RFcommunication with the transponder (which may include a fob), orcommunications between an initiator and a target enabled by near fieldcommunications (NFC). Typical devices may include, for example, a keyring, tag, card, cell phone, wristwatch or any such form capable ofbeing presented for interrogation. Moreover, the system, computing unitor device discussed herein may include a “pervasive computing device,”which may include a traditionally non-computerized device that isembedded with a computing unit. Examples may include watches, Internetenabled kitchen appliances, restaurant tables embedded with RF readers,wallets or purses with imbedded transponders, etc. Furthermore, a deviceor financial transaction instrument may have electronic andcommunications functionality enabled, for example, by: a network ofelectronic circuitry that is printed or otherwise incorporated onto orwithin the transaction instrument (and typically referred to as a “smartcard”); a fob having a transponder and an RFID reader; and/or near fieldcommunication (NFC) technologies. For more information regarding NFC,refer to the following specifications all of which are incorporated byreference herein: ISO/IEC 18092/ECMA-340, Near Field CommunicationInterface and Protocol-1 (NFCIP-1); ISO/IEC 21481/ECMA-352, Near FieldCommunication Interface and Protocol-2 (NFCIP-2); and EMV 4.2 availableat http://www.emvco.com/default.aspx.

The account number may be distributed and stored in any form of plastic,electronic, magnetic, radio frequency, wireless, audio and/or opticaldevice capable of transmitting or downloading data from itself to asecond device. A consumer account number may be, for example, asixteen-digit account number, although each credit provider has its ownnumbering system, such as the fifteen-digit numbering system used byAmerican Express. Each company's account numbers comply with thatcompany's standardized format such that the company using afifteen-digit format will generally use three-spaced sets of numbers, asrepresented by the number “0000 000000 00000”. The first five to sevendigits are reserved for processing purposes and identify the issuingbank, account type, etc. In this example, the last (fifteenth) digit isused as a sum check for the fifteen digit number. The intermediaryeight-to-eleven digits are used to uniquely identify the consumer. Amerchant account number may be, for example, any number or alpha-numericcharacters that identify a particular merchant for purposes of accountacceptance, account reconciliation, reporting, or the like.

Phrases and terms similar to “financial institution” or “transactionaccount issuer” may include any entity that offers transaction accountservices. Although often referred to as a “financial institution,” thefinancial institution may represent any type of bank, lender or othertype of account issuing institution, such as credit card companies, cardsponsoring companies, or third party issuers under contract withfinancial institutions. It is further noted that other participants maybe involved in some phases of the transaction, such as an intermediarysettlement institution.

The terms “payment vehicle,” “financial transaction instrument,”“transaction instrument” and/or the plural form of these terms may beused interchangeably throughout to refer to a financial instrument.

Phrases and terms similar to “internal data” or “closed loop data” mayinclude any data a credit issuer possesses or acquires pertaining to aparticular consumer. Internal data may be gathered before, during, orafter a relationship between the credit issuer and the transactionaccount holder (e.g., the consumer or buyer). Such data may includeconsumer demographic data. Consumer demographic data includes any datapertaining to a consumer. Consumer demographic data may include consumername, address, telephone number, email address, employer and socialsecurity number. Consumer transactional data is any data pertaining tothe particular transactions in which a consumer engages during any giventime period. Consumer transactional data may include, for example,transaction amount, transaction time, transaction vendor/merchant, andtransaction vendor/merchant location. Transaction vendor/merchantlocation may contain a high degree of specificity to a vendor/merchant.For example, transaction vendor/merchant location may include aparticular gasoline filing station in a particular postal code locatedat a particular cross section or address. Also, for example, transactionvendor/merchant location may include a particular web address, such as aUniform Resource Locator (“URL”), an email address and/or an InternetProtocol (“IP”) address for a vendor/merchant. Transactionvendor/merchant, and transaction vendor/merchant location may beassociated with a particular consumer and further associated with setsof consumers. Consumer payment data includes any data pertaining to aconsumer's history of paying debt obligations. Consumer payment data mayinclude consumer payment dates, payment amounts, balance amount, andcredit limit. Internal data may further comprise records of consumerservice calls, complaints, requests for credit line increases,questions, and comments. A record of a consumer service call includes,for example, date of call, reason for call, and any transcript orsummary of the actual call.

Phrases similar to a “payment processor” or “processor” may include acompany (e.g., a third party) appointed (e.g., by a merchant) to handletransactions for merchant banks. Payment processors may be broken downinto two types: front-end and back-end. Front-end payment processorshave connections to various transaction accounts and supplyauthorization and settlement services to the merchant banks' merchants.Back-end payment processors accept settlements from front-end paymentprocessors and, via The Federal Reserve Bank, move money from an issuingbank to the merchant bank. In an operation that will usually take a fewseconds, the payment processor will both check the details received byforwarding the details to the respective account's issuing bank or cardassociation for verification, and may carry out a series of anti-fraudmeasures against the transaction. Additional parameters, including theaccount's country of issue and its previous payment history, may be usedto gauge the probability of the transaction being approved. In responseto the payment processor receiving confirmation that the transactionaccount details have been verified, the information may be relayed backto the merchant, who will then complete the payment transaction. Inresponse to the verification being denied, the payment processor relaysthe information to the merchant, who may then decline the transaction.

Phrases similar to a “payment gateway” or “gateway” may include anapplication service provider service that authorizes payments fore-businesses, online retailers, and/or traditional brick and mortarmerchants. The gateway may be the equivalent of a physical point of saleterminal located in most retail outlets. A payment gateway may protecttransaction account details by encrypting sensitive information, such astransaction account numbers, to ensure that information passes securelybetween the consumer and the merchant and also between merchant andpayment processor.

We claim:
 1. A method, comprising: identifying, by a computing device, aplurality of data structures where each data structure corresponds anindividual consumer transaction account, each consumer transactionaccount configured to perform transactions with point-of-sale (POS)devices of a plurality of merchants; determining, by the computingdevice, a set of plurality of consumer transaction accounts from theplurality of consumer transaction accounts that were used to initiatetransactions with the POS devices of a set of the plurality ofmerchants; generating, by the computing device, a merchant datastructure, the merchant data structure comprising information related toeach merchant in the set of the plurality of merchants and informationcorresponding to the initiated transactions; analyzing, by the computingdevice, each of the initiated transactions, and based on said analysis,determining transaction information indicating a connectivity, activity,common consumers and merchant over-index; determining, by the computingdevice, a score for each of the merchants in the set of the plurality ofmerchants based on the determined transaction information; modifying, bythe computing device, the merchant data structure based on thedetermined scores, the modification of the merchant data structurecomprising removing the merchant information and initiated transactioninformation from the merchant data structure for merchants having scoresbelow a threshold; analyzing, by the computing device, the modifiedmerchant data structure, and determining interest associated withmerchants remaining in the modified merchant data structure;associating, by the computing device, the determined interest with apersona, said persona corresponding to each of the remaining merchantsin the modified merchant data structure; receiving, by the computingdevice and from a consumer over a network, a request for recommendationsof merchants based on the persona; communicating, by the computingdevice over the network in response to the received request, a list ofrecommended merchants, the list of recommended merchants correspondingto the merchants identified in the modified merchant data structure;detecting, by the computing device, a location of a mobile device of theconsumer, in response to the receiving the request for recommendationsfrom the mobile device of the consumer; monitoring, by the computingdevice, the location of the mobile device of the consumer; andadjusting, by the computing device, the list of recommended merchantsassociated with the persona based on the monitoring the location of themobile device of the consumer.
 2. The method of claim 1, wherein themerchant over-index includes a ratio of consumers with the transactionsat a merchant compared to a baseline population of the consumers.
 3. Themethod of claim 1, further comprising creating, by the computing device,a group containing the plurality of merchants based on a common trait,wherein the common trait includes at least one of a similar industrycode or criteria.
 4. The method of claim 1, further comprising creating,by the computing device, a group containing the plurality of merchantsbased on a common trait, wherein the common trait includes a keywordassociation, wherein the keyword association is contained in names ofthe plurality of merchants.
 5. The method of claim 1, further comprisingcreating, by computing device, a group containing the plurality ofmerchants based on a common trait within a time period, wherein thecommon trait includes at least one of items sold, offers, behaviors, ortransaction information.
 6. The method of claim 1, wherein theconnectivity is based on a z-score, and wherein the z-score includes anumber of distinct merchants connected by a common customer, reduced byan average of distinct merchants connected by the common customer, thendivided by a standard deviation.
 7. The method of claim 1, wherein theinterest is determined based on a first trend in at least one oftransaction data or a second trend in social data.
 8. The method ofclaim 1, further comprising adjusting, by the computing device, the listof recommended merchants associated with the persona based on at leastone of a time of day or a percentage of the transactions at a meal-timeexceeding a percentage of overall transactions.
 9. The method of claim1, further comprising determining, by the computing device, a pool ofmerchants comprising a first subset of merchants that are associatedwith a seed merchant and a second subset of merchants of which a seedcustomer has transacted.
 10. The method of claim 1, further comprisingevaluating, by the computing device, the plurality of merchants todetermine a seed merchant based on the interest, wherein the seedmerchant has a merchant profile with a first plurality of attributesthat at least partially match parameters associated with the at leastone of the interest or the persona.
 11. The method of claim 1, wherein aseed customer has a customer profile that includes a second plurality ofattributes that at least partially match parameters associated with theat least one of the interest or the persona.
 12. The method of claim 1,further comprising defining, by the computing device, the interest basedon at least one of transaction data or social data, wherein the socialdata is at least one of a hashtag, a social interest, or a keyword. 13.The method of claim 1, further comprising performing, by the computingdevice, an additional analysis cycle in response to a turnover indexbeing greater than a turnover index threshold.
 14. The method of claim1, further comprising determining, by the computing device, a group ofboost consumers, wherein the determining the group of boost consumerscomprises: evaluating the transactions across a subset of the pluralityof merchants associated with the interest, wherein the subset of theplurality of merchants have the score above the threshold establishedfor the interest; and retrieving the consumers that have thetransactions with two or more distinct merchants from a list of updatedmerchants to create the group of boost consumers.
 15. The method ofclaim 1, wherein the interest is defined by a list of initial merchantsretrieved from a merchant database, wherein the list of initialmerchants are obtained based on at least one of the keyword association,industry codes, or explicit queries.
 16. The method of claim 1, whereinthe interest is defined by the consumer having transaction data andsocial data associated with a keyword associated with the interest. 17.The method of claim 1, further comprising: normalizing, by the computingdevice, the scoring, based on a set of at least one of the plurality ofmerchants or the consumers; and ranking, by the computing device, theplurality of merchants based on the normalizing.
 18. An article ofmanufacture including a non-transitory, tangible computer readablestorage medium having instructions stored thereon that, in response toexecution by a computing device, cause the computing device to performoperations comprising: identifying, by the computing device, a pluralityof data structures where each data structure corresponds an individualconsumer transaction account, each consumer transaction accountconfigured to perform transactions with point-of-sale (POS) devices of aplurality of merchants; determining, by the computing device, a set ofplurality of consumer transaction accounts from the plurality ofconsumer transaction accounts that were used to initiate transactionswith the POS devices of a set of the plurality of merchants; generating,by the computing device, a merchant data structure, the merchant datastructure comprising information related to each merchant in the set ofthe plurality of merchants and information corresponding to theinitiated transactions; analyzing, by the computing device, each of theinitiated transactions, and based on said analysis, determiningtransaction information indicating a connectivity, activity, commonconsumers and merchant over-index; determining, by the computing device,a score for each of the merchants in the set of the plurality ofmerchants based on the determined transaction information; modifying, bythe computing device, the merchant data structure based on thedetermined scores, the modification of the merchant data structurecomprising removing the merchant information and initiated transactioninformation from the merchant data structure for merchants having scoresbelow a threshold; analyzing, by the computing device, the modifiedmerchant data structure, and determining interest associated withmerchants remaining in the modified merchant data structure;associating, by the computing device, the determined interest with apersona, said persona corresponding to each of the remaining merchantsin the modified merchant data structure; receiving, by the computingdevice and from a consumer over a network, a request for recommendationsof merchants based on the persona; communicating, by the computingdevice over the network in response to the received request, a list ofrecommended merchants, the list of recommended merchants correspondingto the merchants identified in the modified merchant data structure;detecting, by the computing device, a location of a mobile device of theconsumer, in response to the receiving the request for recommendationsfrom the mobile device of the consumer; monitoring, by the computingdevice, the location of the mobile device of the consumer; andadjusting, by the computing device, the list of recommended merchantsassociated with the persona based on the monitoring the location of themobile device of the consumer.
 19. A system comprising: a processor; anda tangible, non-transitory memory configured to communicate with theprocessor, the tangible, non-transitory memory having instructionsstored thereon that, in response to execution by the processor, causethe processor to perform operations comprising: identifying, by theprocessor, a plurality of data structures where each data structurecorresponds an individual consumer transaction account, each consumertransaction account configured to perform transactions withpoint-of-sale (POS) devices of a plurality of merchants; determining, bythe processor, a set of plurality of consumer transaction accounts fromthe plurality of consumer transaction accounts that were used toinitiate transactions with the POS devices of a set of the plurality ofmerchants; generating, by the processor, a merchant data structure, themerchant data structure comprising information related to each merchantin the set of the plurality of merchants and information correspondingto the initiated transactions; analyzing, by the processor, each of theinitiated transactions, and based on said analysis, determiningtransaction information indicating a connectivity, activity, commonconsumers and merchant over-index; determining, by the processor, ascore for each of the merchants in the set of the plurality of merchantsbased on the determined transaction information; modifying, by theprocessor, the merchant data structure based on the determined scores,the modification of the merchant data structure comprising removing themerchant information and initiated transaction information from themerchant data structure for merchants having scores below a threshold;analyzing, by the processor, the modified merchant data structure, anddetermining interest associated with merchants remaining in the modifiedmerchant data structure; associating, by the processor, the determinedinterest with a persona, said persona corresponding to each of theremaining merchants in the modified merchant data structure; receiving,by the processor and from a consumer over a network, a request forrecommendations of merchants based on the persona; communicating, by theprocessor over the network in response to the received request, a listof recommended merchants, the list of recommended merchantscorresponding to the merchants identified in the modified merchant datastructure; detecting, by the processor, a location of a mobile device ofthe consumer, in response to the receiving the request forrecommendations from the mobile device of the consumer; monitoring, bythe processor, the location of the mobile device of the consumer; andadjusting, by the processor, the list of recommended merchantsassociated with the persona based on the monitoring the location of themobile device of the consumer.